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The Corticospinal Excitability Can Be Predicted by Spontaneous Electroencephalography Oscillations.
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2021-08-23 , DOI: 10.3389/fnins.2021.722231
Guiyuan Cai 1, 2 , Manfeng Wu 1, 2 , Qian Ding 1, 3 , Tuo Lin 1, 3 , Wanqi Li 1, 3 , Yinghua Jing 1, 3 , Hongying Chen 1, 2 , Huiting Cai 4 , Tifei Yuan 4, 5, 6 , Guangqing Xu 7 , Yue Lan 1, 3
Affiliation  

Transcranial magnetic stimulation (TMS) has a wide range of clinical applications, and there is growing interest in neural oscillations and corticospinal excitability determined by TMS. Previous studies have shown that corticospinal excitability is influenced by fluctuations of brain oscillations in the sensorimotor region, but it is unclear whether brain network activity modulates corticospinal excitability. Here, we addressed this question by recording electroencephalography (EEG) and TMS measurements in 32 healthy individuals. The resting motor threshold (RMT) and active motor threshold (AMT) were determined as markers of corticospinal excitability. The least absolute shrinkage and selection operator (LASSO) was used to identify significant EEG metrics and then correlation analysis was performed. The analysis revealed that alpha2 power in the sensorimotor region was inversely correlated with RMT and AMT. Innovatively, graph theory was used to construct a brain network, and the relationship between the brain network and corticospinal excitability was explored. It was found that the global efficiency in the theta band was positively correlated with RMT. Additionally, the global efficiency in the alpha2 band was negatively correlated with RMT and AMT. These findings indicated that corticospinal excitability can be modulated by the power spectrum in sensorimotor regions and the global efficiency of functional networks. EEG network analysis can provide a useful supplement for studying the association between EEG oscillations and corticospinal excitability.

中文翻译:

皮质脊髓兴奋性可以通过自发脑电图振荡预测。

经颅磁刺激 (TMS) 具有广泛的临床应用,并且对由 TMS 确定的神经振荡和皮质脊髓兴奋性越来越感兴趣。先前的研究表明,皮质脊髓兴奋性受感觉运动区大脑振荡波动的影响,但尚不清楚大脑网络活动是否调节皮质脊髓兴奋性。在这里,我们通过记录 32 名健康个体的脑电图 (EEG) 和 TMS 测量值来解决这个问题。静息运动阈值 (RMT) 和活动运动阈值 (AMT) 被确定为皮质脊髓兴奋性的标志物。使用最小绝对收缩和选择算子 (LASSO) 来识别重要的 EEG 指标,然后进行相关分析。分析表明,感觉运动区域的 alpha2 功率与 RMT 和 AMT 呈负相关。创新性地利用图论构建脑网络,探索脑网络与皮质脊髓兴奋性的关系。发现θ波段的全局效率与RMT呈正相关。此外,alpha2 波段的整体效率与 RMT 和 AMT 呈负相关。这些发现表明皮质脊髓兴奋性可以通过感觉运动区域的功率谱和功能网络的整体效率来调节。脑电网络分析可以为研究脑电振荡与皮质脊髓兴奋性之间的关联提供有用的补充。运用图论构建脑网络,探讨脑网络与皮质脊髓兴奋性的关系。研究发现,theta 波段的全局效率与 RMT 呈正相关。此外,alpha2 波段的整体效率与 RMT 和 AMT 呈负相关。这些发现表明皮质脊髓兴奋性可以通过感觉运动区域的功率谱和功能网络的整体效率来调节。脑电网络分析可以为研究脑电振荡与皮质脊髓兴奋性之间的关联提供有用的补充。运用图论构建脑网络,探讨脑网络与皮质脊髓兴奋性的关系。研究发现,theta 波段的全局效率与 RMT 呈正相关。此外,alpha2 波段的整体效率与 RMT 和 AMT 呈负相关。这些发现表明皮质脊髓兴奋性可以通过感觉运动区域的功率谱和功能网络的整体效率来调节。脑电网络分析可以为研究脑电振荡与皮质脊髓兴奋性之间的关联提供有用的补充。研究发现,theta 波段的全局效率与 RMT 呈正相关。此外,alpha2 波段的整体效率与 RMT 和 AMT 呈负相关。这些发现表明皮质脊髓兴奋性可以通过感觉运动区域的功率谱和功能网络的整体效率来调节。脑电网络分析可以为研究脑电振荡与皮质脊髓兴奋性之间的关联提供有用的补充。研究发现,theta 波段的全局效率与 RMT 呈正相关。此外,alpha2 波段的整体效率与 RMT 和 AMT 呈负相关。这些发现表明皮质脊髓兴奋性可以通过感觉运动区域的功率谱和功能网络的整体效率来调节。脑电网络分析可以为研究脑电振荡与皮质脊髓兴奋性之间的关联提供有用的补充。
更新日期:2021-08-23
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